Using media information for improving direct marketing response rate

ABSTRACT

Embodiments of the invention relate to improving direct marketing response rate through use of media information. One embodiment includes extracting samples of language usage in one or more social media activities. Language usage features comprising relationships to products are derived by analyzing the extracted samples for relevant language terms. The language usage features are mapped to one or more of personality traits, emotional state and personal features. The mapped language usage features and personal features are stored in a repository. Customers are segmented for direct marketing using the mapped language usage features and the personal features.

BACKGROUND

Embodiments of the invention relate to direct marketing, and inparticular, using media linguistic information for improving directmarketing response rate.

Social media are popular avenues for information sharing or exchange.Information sharing systems, such as forums for obtaining productreviews or social messaging systems are sometimes helpful to users onsocial media platforms. People also informally exchange information insocial media and business context through platforms, such as Facebook®and Twitter®. Businesses at times may use social media platforms forannouncing new products or deals on existing products. Users of thesocial media platforms may discuss the newly announced products ordeals. Businesses interact with people through advertising, requests forinformation and customer service.

BRIEF SUMMARY

Embodiments of the invention relate to improving direct marketingresponse rate using media information. One embodiment includesextracting samples of language usage in one or more social mediaactivities. Language usage features comprising relationships to productsare derived by analyzing the extracted samples for relevant languageterms. The language usage features are mapped to one or more ofpersonality traits, emotional state or personal features. The mappedlanguage usage features and personal features are stored in arepository. Customers are segmented for direct marketing using themapped language usage features and the personal features.

Another embodiment comprises extracting samples of language usage in oneor more social media activities. A likelihood of product interest in oneor more products is determined by analyzing the extracted samples for arelationship to a product based on language terms. A response to productmarketing is predicted using a prediction model that is generated basedon the determined likelihood of product interest. Customers aresegmented for direct marketing based on the predicted response.

One embodiment comprises extracting samples of language usage in one ormore social media activities. Time and date information are extractedfor the samples of language usage based on when the one or more socialmedia activities were conducted. Temporal activity patterns are derivedbased on statistics for the extracted time and date information. Theprobability of customer response to a direct marketing activity ismodeled based on derived temporal activity patterns. Segmentation ofcontacts for direct marketing is performed using the modeled probabilityand derived temporal activity patterns.

These and other features, aspects and advantages of the presentinvention will become understood with reference to the followingdescription, appended claims and accompanying figures.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example system for using mediainformation for improving direct marketing response rate, in accordancewith an embodiment of the invention;

FIG. 2 illustrates a block diagram of an example client/server systemutilizing media information for improving direct marketing responserate, in accordance with an embodiment of the invention;

FIG. 3 is a block diagram showing a process for using media informationfor improving direct marketing response rate, in accordance with anembodiment of the invention;

FIG. 4 is a block diagram illustrating an example of a networkenvironment for using media information for improving direct marketingresponse rate, according to an embodiment of the present invention; and

FIG. 5 is a block diagram illustrating an example of a server includinga system utilizing media information for improving direct marketingresponse rate, according to an embodiment of the present invention, asshown in FIG. 4.

DETAILED DESCRIPTION

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

Referring now to the drawings, FIG. 1 shows an implementation of system100 for using media (e.g., social media) information for improvingdirect marketing response rate. In one embodiment, system 100 comprisesa server or client processor 105 including an extraction module 110, amapping module 120, a derivation module 130, a time and/or date activitypattern module 140 and a segmentation module 150. System 100 furtherincludes a repository 160 (e.g., storage device(s), memory device(s),virtual memory, etc.) and media activity sources 1-N 170. In oneembodiment the repository may comprise a storage device or medium thatis directly, indirectly or virtually connected to the system 100. In oneembodiment, the media activity sources 1-N 170 may be coupled to thesystem 100 via a network (wireless, wired, etc.). In one example, themedia activity sources 1-N 170 may comprise written and verbal sources,such as multiple social media platforms, websites, written/verbalsurveys, call centers, etc. In one embodiment, the system 100 collects,organizes and analyzes language samples on a per-person basis, andgenerates personal language usage features for individuals.

In one embodiment, the extraction module 110 provides extraction ofsamples of language usage in one or more media activities from the mediasources 1-N 170, such as chatting, messaging, commenting, posting,replying to questions, replying to topics, providing feedback, providingproduct reviews, conducting written/verbal surveys, responding to a callcenter questionnaire, text from personal web pages, etc. In oneembodiment, the extraction module 110 searches written text or speechresponses using key words, such as product names, competing productnames, text/speech related to products or product fields, ownership,intent to purchase, intent to sell, etc., for extracting text samples.In another embodiment, the extraction module 110 searches for specificuser written activities including comments based on topics or fromfollowing postings from one or more media activities. Speech responsesmay also be searched using known speech recognition techniques andspeech-to-text conversion techniques. In one embodiment, the extractionmodule 110 extracts samples of language use at predetermined intervals(e.g., once a month, twice a month, once a week, etc.).

In one example, the specific users are previous customers, potentialcustomers, competing product customers, or any combination of the three.The specific users' information may be collected based on customerquestionnaires, profiles, requested information, etc. In one example,businesses may maintain databases of customers/potential customersprofiles that may include customer information, such as contactinformation, name, demographics (e.g., age, location, education, income,etc.), social media websites that are visited/used, usernames of socialmedia platforms, email addresses, etc. In one embodiment, the specificusers' information is stored in the repository 160.

In one embodiment, the derivation module 130 derives language usagefeatures from samples of language usage extracted samples (e.g., text,converted speech-to-text, etc.) with respect to various relationships torelevant products for derivation of usage features. In one example,usage features may be derived based on a pre-defined taxonomy of productnames, and text may be searched for product mentions, with analysis andsearching for nearby words to indicate a like, dislike, or intent topurchase these products, etc. In another example, the derived usagefeatures may be described as numbers indicating product mentions, bothin general and also in terms of relationships, such as a like, adislike, ownership, intent to purchase, etc. In one embodiment, thederived language features may be used for prediction modeling to computea probability of customer response to direct marketing techniques.

In one embodiment, the mapping module 120 uses the derived languageusage for analysis with respect to various relationships to relevantproducts for mapping usage features. In one embodiment, the samples maybe analyzed using content-dependent and content-independent analysis. Inone example, content-dependent analysis relates to what a person issaying/writing. Content-dependent analysis searches for product/brandmentions, or text indicating something about a person's activities(e.g., from an application, messages that may indicate that users arecurrently busy). In one example, content-independent analysis doesn'trelate to what a person is talking/writing about. For example,content-independent analysis may comprise measuring the times of mediausage, for indicating availability, regardless of what a person says orwrites. In one example, content-independent analysis may measure theLinguistic Inquiry and Word Count (LIWC) features, which describelanguage use in terms of how frequently people use 68 categories ofwords. These low-level LIWC language features may be mapped tohigher-level personality features (e.g., Big5 (e.g., openness,conscientiousness, extraversion, agreeableness, and neuroticism), orBig5+facets). Another example may comprise word-length and sentencelength, which may be used by tests such as the Flesch-Kincaidreadability and grade-level measures, which use sentence and wordlengths to indicate readability and the grade-level at which a personwrites. Many possible low-level language features exist, based onindividual word use, phrase use, sentence structure, etc. One goal ofcontent-independent analysis is to measure language features and mapthem to general personal features, such as personality, emotional state,level of education, or anything else for which a metric exists.

In one example, usage features may be mapped based on a pre-definedtaxonomy of product names, and text may be searched for productmentions, with analysis and searching for nearby words to indicate alike, dislike, or intent to purchase these products, etc. In anotherexample, the usage features may be described as numbers indicatingproduct mentions, both in general and also in terms of relationships,such as a like, a dislike, ownership, intent to purchase, etc. In oneembodiment, the language features may be used for prediction modeling tocompute a probability of customer response to direct marketingtechniques. In one embodiment, language usage may be mapped to emotionalstate using known techniques to infer content provider's transientemotional state from language use.

In one embodiment, the time and/or date activity pattern module 140extracts time and date information regarding when the media activitieswere conducted, and derives activity patterns based on statistics forthe extracted time and date information. The temporal aspects of postingactivity are used to infer the times of day and day of week that aperson is most likely to be using media activity sources 1-N 170.

In one example, scores may be associated with particular users based onlearned language usage and determined traits, such as personality andemotional state. In one example, personality, emotional state,product-relationship, and temporal features derived through analysis arestored in the repository 160 for supplementing existing customer andpotential customer demographic information for segmentation of customersand/or potential customers for use in direct marketing. In oneembodiment, language usage may be analyzed for determining potentialcustomers based on analysis of communications between current customersand their friends, family members, business associates, etc. In oneexample, communications may include conversations or comments related tointent to purchase products, likes/dislikes of products, etc. Thisinformation may be used to determine other individuals to targetmarketing information to either through present customers (e.g., forrelaying the marketing information), or directly via obtaining userinformation (e.g., user name of a media platform).

In one embodiment, the time and/or date activity pattern module 140derives temporal features for timing of offers to particular customersor potential customers of products or services to improve potentialresponse rate for direct marketing. In one example, marketing schemesmay include making offers via e-mail, social networks or telephonemarketing. Contacting people when they are most likely to be using theparticular medium based on personal schedules or habits may increase thelikelihood of a positive response to direct marketing information. Inone example, if a derived media usage pattern is determined,probabilities or scores may be computed for days of the week and timesof day that a user will likely be using media, such as using acomputer/cell phone/tablet/etc. to check email, using a social mediaplatform(s), checking messages/chats/tweets/etc. The determinedprobabilities may then be used for marketing criteria, for example,criteria for sending an offer over the next 3 days, contacting eachperson at the next time period where the person is >40% likely to beactive on a particular social media platform. Temporal patterns ofcustomers and/or potential customers may assist in reducing marketingcosts since marketing schemes can be targeted to certain days and timesbased on temporal patterns (as compared to marketing schemes thattargeted all individuals simultaneously at a certain time or date).

In one example, the time and/or date activity pattern module 140 uses amodel that is trained from deriving patterns of users in previous mediaactivities (e.g., written/text and/or speech). In one example,information regarding the time interval during the day and theparticular day of the week is used. For each time interval and day ofthe week, a user may have a specific media activity usage behavior thatis used for training/learning via a statistical model. In one example,once the model learns usage patterns, the time and/or date activitypattern module 140 predicts a probability or score for each customer orpotential customer at a given time on a given day of the week. Based onthe learned information, the probability of receipt and viewing ofdirect marketing information by a customer or potential customer isincreased, along with potential for increasing the response rate to themarketing information.

In one embodiment, the time and/or date activity pattern module 140 usesheuristics for predicting whether the customer or potential customerwill be likely to perform media activities. In one example, users thatrecently used particular domain-specific words in their content forstatus messages (e.g., a connectivity problem, a cell phone batteryproblem, etc.), or users that have recently sent status updates withintheir respective social network (e.g., messages that may indicate thatthe users are currently busy or not ready to receive direct marketinginformation), are identified as not available for receiving marketinginformation. In one example, the time and/or date activity patternmodule 140 may use a user's past time usage to determine to the extentthe person may be interrupted from other tasks, availability means, suchas device readiness and connectivity. In one example, time and/or dateactivity pattern module 140 filters out customers or potential customersthat are not ready to receive marketing information and stores thecustomers and potential customers that are deemed ready to receivemarketing information in the repository 160.

In one embodiment, the segmentation module 150 enhances marketingactivities that involve manual segmentation based on demographics by theaddition of further filtering criteria. Marketers target people thatthey think believe will respond to marketing information, programs,commercials, coupons, incentives, etc. For example, a product andmarketing scheme may relate to demographics of 18-24 year olds in largemetropolitan cities. Embodiments of the invention provide for enhancedsegmentation using the segmentation module 150 by further segmentingdemographic segmentation by using personality, emotional state, andproduct relationship features for finer segmentation. In one example,the segmentation module 150 provides for enhanced segmentation bytargeting high-openness and high-extraversion individuals that mentiontargeted products positively in language usage samples, but do not ownthe targeted product. In one example, a sample of language usage may bedetermined to include language relating users being stressed and unhappyand that may have also mentioned chocolate in a media activity. Thisinformation may be used for providing segmentation in order to directmarketing of chocolate ice cream coupons for direct marketing. Inanother example, online search advertisers may pay to have advertisingshown next to certain search terms. The use of personality and emotionaltraits and language features would allow online search advertisers toask for personality types and emotional states in addition to searchterms for online advertising.

In one embodiment, the mapping of personality traits and emotionalstate, derived language features and temporal activity patterns, whenused with known demographics may be used for segmenting customer andpotential customer populations for test marketing campaigns. The resultof the test campaign may then be used for future direct marketing andfuture segmentation based on results and determined correlations betweenthe demographics and personality traits and emotional state, derivedlanguage features and temporal activity patterns.

FIG. 2 illustrates a block diagram for a system 200, such as a networkplatform that employs system 100 for using media information forimproving direct marketing response rate. In one embodiment, the system200 includes multiple client devices 210 l-n, multiple server devices220 l-n, and multiple storage devices 230 l-n, where n is a positivenumber greater than 1. In one example, the system 100 may be used onlyon client devices 210, only on server devices 220, or on both clientdevices 210 and server devices 220. In one example, the server devices220 run the network platform and users use the client devices 210 toaccess the network.

FIG. 3 illustrates a flowchart of an example process 300 for using mediainformation (e.g., social media information) for improving directmarketing response rate, according to one embodiment. In process block310, one or more samples of language use are extracted, for example, bythe extraction module 110 in FIG. 1. In process block 320, languageusage features are derived from the language usage is mapped to one ormore of personality traits and emotional states, for example, from theextracted language usage, for example, by the derivation module 130 ofsystem 100. In process block 330, language usage features are mapped toone or more of personality traits and emotional states, for example, bythe mapping module 120 of system 100. In process block 340, the mappedlanguage features are stored as personal features in a repository, suchas repository 160 of system 100. In process block 350 the personalfeatures are used for segmentation of customers (and/or potentialcustomers) for direct marketing techniques, for example, by thesegmentation module 150 of system 100.

In one embodiment, process 300 may further include determining time anddate activity patterns of media activities performed by customers and/orpotential customers for use in further segmentation of customers orpotential customers for use in direct marketing, for example, by thetime and/or date activity pattern module 140.

FIG. 4 illustrates an example of the basic components of an informationtechnology system 10 utilizing system 100, according to an embodiment ofthe present invention. The information technology system 10 includes aserver 11 and remote devices 15 and 17-20 that may utilize the system100 of the present invention. In one embodiment, the server 11implements the system 100 of the present invention.

Each of the remote devices 15 and 17-20 has applications and can have alocal database 16. Server 11 contains applications, and is connected toa database 12 that can be accessed by remote device 15 and 17-20 viaconnections 14(A-F), respectively, over a network 13. The server 11executes software for a computer network and controls access to itselfand database 12. The remote devices 15 and 17-20 may access the database12 over the network 13, such as but not limited to: the Internet, alocal area network (LAN), a wide area network (WAN), via a telephoneline using a modem (POTS), Bluetooth, WiFi, WiMAX, cellular, optical,satellite, RF, Ethernet, magnetic induction, coax, RS-485, the like orother like networks. The server 11 may also be connected to the localarea network (LAN) within an organization.

The remote devices 15 and 17-20 may each be located at remote sites.Remote device 15 and 17-20 include but are not limited to, PCs,workstations, laptops, handheld computers, pocket PCs, PDAs, pagers, WAPdevices, non-WAP devices, cell phones, palm devices, printing devices,and the like. Included with each remote device 15 and 17-20 is anability to request relevant material from a large collection ofdocuments via search queries to the server 11. Thus, when a user at oneof the remote devices 15 and 17-20 desires to access the system 100 andthe database 12 at the server 11, the remote device 15 and 17-20communicates over the network 13, to access the system 100, the server11 and database 12.

Third party computer systems 21 and databases 22 can be accessed by theserver 11 in order to provide access to additional collections ofdocuments and/or search indexes. Data that is obtained from third partycomputer systems 21 and database 22 can be stored on server 11 anddatabase 12 in order to provide later access to the user on remotedevices 15 and 17-20. It is also contemplated that for certain types ofdata, the remote devices 15 and 17-20 can access the third partycomputer systems 21 and database 22 directly using the network 13.

The system 100 utilizes a process for using media information forimproving direct marketing response rate, according to an embodiment ofthe invention. Illustrated in FIG. 5 is a block diagram demonstrating anexample of server 11, as shown in FIG. 4, utilizing the system 100according to an embodiment of the present invention. The server 11includes, but is not limited to, PCs, workstations, laptops, PDAs, palmdevices, and the like. The processing components of the third partycomputer systems are similar to that of the description for the server11 (FIG. 5).

Generally, in terms of hardware architecture, as shown in FIG. 5, theserver 11 includes a processor 41, a computer readable medium such asmemory 42, and one or more input and/or output (I/O) devices (orperipherals) that are communicatively coupled via a local interface 43.The local interface 43 can be, for example but not limited to, one ormore buses or other wired or wireless connections, as is known in theart. The local interface 43 may have additional elements, which areomitted for simplicity, such as controllers, buffers (caches), drivers,repeaters, and receivers to enable communications. Further, the localinterface 43 may include address, control, and/or data connections toenable appropriate communications among the aforementioned components.

The processor 41 is a hardware device for executing software that can bestored in memory 42. The processor 41 can be virtually any custom madeor commercially available processor, a central processing unit (CPU),data signal processor (DSP) or an auxiliary processor among severalprocessors associated with the server 11, and a semiconductor basedmicroprocessor (in the form of a microchip) or a microprocessor.

The memory 42 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM), such as dynamic randomaccess memory (DRAM), static random access memory (SRAM), etc.) andnonvolatile memory elements (e.g., read only memory (ROM), erasableprogrammable read only memory (EPROM), electronically erasableprogrammable read only memory (EEPROM), programmable read only memory(PROM), tape, compact disc read only memory (CD-ROM), disk, diskette,cartridge, cassette or the like, etc.). Moreover, the memory 42 mayincorporate electronic, magnetic, optical, and/or other types of storagemedia. Note that the memory 42 can have a distributed architecture,where various components are situated remote from one another, but canbe accessed by the processor 41.

The software in memory 42 may include one or more separate programs,each of which comprises an ordered listing of executable instructionsfor implementing logical functions. In the example illustrated in FIG.5, the software in the memory 42 includes a suitable operating system(O/S) 51 and the search system 100 of the present invention. The system100 comprises functional components and process blocks described furtherbelow.

The operating system 51 essentially controls the execution of othercomputer programs, such as the system 100, and provides scheduling,input/output control, file and data management, memory management, andcommunication control and related services. However, the system 100 ofthe present invention is applicable on all other commercially availableoperating systems.

The system 100 may comprise a source program, executable program (objectcode), script, or any other entity comprising a set of computer programinstructions to be performed. When the system 100 is a source program,then the program is usually translated via a compiler, assembler,interpreter, or the like, which may or may not be included within thememory 42, so as to operate properly in connection with the O/S 51.Furthermore, the system 100 can be written as (a) an object orientedprogramming language, which has classes of data and methods, or (b) aprocedure programming language, which has routines, subroutines, and/orfunctions. The computer program instructions may execute entirely onserver 11, partly on the server 11, as a stand-alone software package,partly on server 11 and partly on a remote computer or entirely on theremote computer or server. In the latter scenario, the remote computermay be connected to the user's computer through any type of network,including a local area network (LAN) or a wide area network (WAN), orthe connection may be made to an external computer (for example, throughthe Internet using an Internet Service Provider).

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The I/O devices may include input devices, for example but not limitedto, a mouse 44, keyboard 45, scanner (not shown), microphone (notshown), etc. Furthermore, the I/O devices may also include outputdevices, for example but not limited to, a printer (not shown), display46, etc. Finally, the I/O devices may further include devices thatcommunicate both inputs and outputs, for instance but not limited to, aNIC or modulator/demodulator 47 (for accessing remote devices, otherfiles, devices, systems, or a network), a radio frequency (RF) or othertransceiver (not shown), a telephonic interface (not shown), a bridge(not shown), a router (not shown), etc.

If the server 11 is a PC, workstation, intelligent device or the like,the software in the memory 42 may further include a basic input outputsystem (BIOS) (omitted for simplicity). The BIOS is a set of essentialsoftware routines that initialize and test hardware at startup, startsthe O/S 51, and supports the transfer of data among the hardwaredevices. The BIOS is stored in some type of read-only-memory, such asROM, PROM, EPROM, EEPROM or the like, so that the BIOS can be executedwhen the server 11 is activated.

When the server 11 is in operation, the processor 41 is configured toexecute software stored within the memory 42, to communicate data to andfrom the memory 42, and generally to control operations of the server 11pursuant to the software. The system 100 and the O/S 51 are read, inwhole or in part, by the processor 41, perhaps buffered within theprocessor 41, and then executed.

In the context of this document, a “computer-readable medium” can be anymeans that can store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device. The computer readable medium can be, for examplebut not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, propagationmedium, or other physical device or means that can contain or store acomputer program for use by or in connection with a computer relatedsystem or method.

When the system 100 is implemented in software, as is shown in FIG. 5,it should be noted that the system 100 can be embodied in anycomputer-readable medium for use by or in connection with an instructionexecution system, apparatus, or device, such as a computer-based system,processor-containing system, or other system that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

In the context of this document, a “computer-readable medium” can be anymeans that can store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device. The computer readable medium can be, for examplebut not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, propagationmedium, or other physical device or means that can contain or store acomputer program for use by or in connection with a computer relatedsystem or method.

More specific examples (a non-exhaustive list) of the computer-readablemedium would include the following: an electrical connection(electronic) having one or more wires, a portable computer diskette(magnetic or optical), a random access memory (RAM) (electronic), aread-only memory (ROM) (electronic), an erasable programmable read-onlymemory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber(optical), and a portable compact disc memory (CDROM, CD R/W) (optical).Note that the computer-readable medium could even be paper or anothersuitable medium, upon which the program is printed or punched (as inpaper tape, punched cards, etc.), as the program can be electronicallycaptured, via for instance optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

In an alternative embodiment, where the system 100 is implemented inhardware, the system 100 can be implemented with any one or acombination of the following technologies, which are each well known inthe art: a discrete logic circuit(s) having logic gates for implementinglogic functions upon data signals, an application specific integratedcircuit (ASIC) having appropriate combinational logic gates, aprogrammable gate array(s) (PGA), a field programmable gate array(FPGA), etc.

The remote devices 15 and 17-20 provide access to the system 100 of thepresent invention on server 11 and database 12 using for example, butnot limited to, an Internet browser. The information accessed in server11 and database 12 can be provided in a number of different formsincluding, but not limited to, ASCII data, WEB page data (i.e., HTML),XML or other type of formatted data.

As illustrated, the remote device 15 and 17-20 are similar to thedescription of the components for server 11 described with regard toFIG. 5. The remote devices 15 and 17-20 are referred to as remotedevices 15 for the sake of brevity.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should be emphasized that the above-described embodiments of thepresent invention, particularly, any “preferred” embodiments, are merelypossible examples of implementations, merely set forth for a clearunderstanding of the principles of the invention.

Many variations and modifications may be made to the above-describedembodiment(s) of the invention without departing substantially from thespirit and principles of the invention. All such modifications andvariations are intended to be included herein within the scope of thisdisclosure and the present invention and protected by the followingclaims.

What is claimed is:
 1. A method comprising: at a server running one ormore online social media platforms: extracting samples of language usagefrom one or more social media activities involving users accessing theone or more online social media platforms via client devices;determining a likelihood of interest in a product by analyzing theextracted samples for a relationship to the product based on languageterms of the extracted samples; training a prediction model based on theextracted samples and the determined likelihood of interest in theproduct; predicting a response to marketing of the product using theprediction model; identifying one or more of the users who positivelymention the product in the extracted samples but do not own the product;and increasing a probability of the identified one or more usersreceiving and viewing direct marketing of the product via the one ormore online social media platforms by: determining whether theidentified one or more users are available to receive and view thedirect marketing of the product via the one or more online social mediaplatforms based on device readiness of one or more client devices theidentified one or more users use to access the one or more online socialmedia platforms, and device connectivity of the one or more clientdevices; and in response to determining the identified one or more usersare available to receive and view the direct marketing of the productvia the one or more online social media platforms: determiningparticular days of week and times of day the identified one or moreusers are most likely to access the one or more online social mediaplatforms via one or more client devices based on the predicted responseand time and date information for the extracted samples; and timingdelivery of one or more electronic offers of the product to theidentified one or more users via the one or more online social mediaplatforms in accordance with the determined particular days of week andtimes of day.
 2. The method of claim 1, wherein the extracted samplescomprise one or more of a mention of the product, a mention of acompeting product of the product, and a mention of an intent to purchasethe product.
 3. The method of claim 2, wherein the one or more socialmedia activities comprise one or more of a social media writing activityand a speech interaction.
 4. The method of claim 3, wherein therelationship to the product comprises one or more of an indication ofliking the product, an indication of disliking the product, anindication of ownership of the product, and an indication of an intentto purchase the product.
 5. The method of claim 4, wherein determining alikelihood of interest in a product further comprises: mapping thelanguage usage to one or more of personality traits and emotionalstates.
 6. The method of claim 5, wherein determining a likelihood ofinterest in a product further comprises: using one or more of Big 5 andlinguistic inquiry and word count (LIWC) techniques for the mapping. 7.The method of claim 5, further comprising: enhancing online searchadvertising by associating search terms with one or more of thepersonality traits and the emotional states.
 8. The method of claim 5,further comprising: modeling a probability of user response to thedirect marketing of the product using test marketing heuristics and thedetermined likelihood of interest in the product.
 9. The method of claim5, further comprising: extracting the time and date information for theextracted samples based on when the one or more social media activitieswere conducted; and deriving temporal activity patterns based onstatistics for the time and date information for the extracted samples,wherein the determined particular days of week and times of day isfurther based on the derived temporal activity patterns.
 10. A systemcomprising a computer processor, a computer-readable hardware storagemedium, and program code embodied with the computer-readable hardwarestorage medium for execution by the computer processor to implement amethod comprising: at a server running one or more online social mediaplatforms: extracting samples of language usage from one or more socialmedia activities involving users accessing the one or more online socialmedia platforms via client devices; determining a likelihood of interestin a product by analyzing the extracted samples for a relationship tothe product based on language terms of the extracted samples; training aprediction model based on the extracted samples and the determinedlikelihood of interest in the product; predicting a response tomarketing of the product using the prediction model; identifying one ormore of the users who positively mention the product in the extractedsamples but do not own the product; and increasing a probability of theidentified one or more users receiving and viewing direct marketing ofthe product via the one or more online social media platforms by:determining whether the identified one or more users are available toreceive and view the direct marketing of the product via the one or moreonline social media platforms based on device readiness of one or moreclient devices the identified one or more users use to access the one ormore online social media platforms, and device connectivity of the oneor more client devices; and in response to determining the identifiedone or more users are available to receive and view the direct marketingof the product via the one or more online social media platforms:determining particular days of week and times of day the identified oneor more users are most likely to access the one or more online socialmedia platforms via one or more client devices based on the predictedresponse and time and date information for the extracted samples; andtiming delivery of one or more electronic offers of the product to theidentified one or more users via the one or more online social mediaplatforms in accordance with the determined particular days of week andtimes of day.
 11. The system of claim 10, wherein the extracted samplescomprise one or more of a mention of the product, a mention of acompeting product of the product, and a mention of an intent to purchasethe product.
 12. The system of claim 11, wherein the one or more socialmedia activities comprise one or more of a social media writing activityand a speech interaction.
 13. The system of claim 12, wherein therelationship to the product comprises one or more of an indication ofliking the product, an indication of disliking the product, anindication of ownership of the product, and an indication of an intentto purchase the product.
 14. The system of claim 13, wherein determininga likelihood of interest in a product further comprises: mapping thelanguage usage to one or more of personality traits and emotionalstates.
 15. The system of claim 14, wherein determining a likelihood ofinterest in a product further comprises: using one or more of Big 5 andlinguistic inquiry and word count (LIWC) techniques for the mapping. 16.The system of claim 14, wherein the method further comprises: enhancingonline search advertising by associating search terms with one or moreof the personality traits and the emotional states.
 17. The system ofclaim 14, wherein the method further comprises: modeling a probabilityof user response to the direct marketing of the product using testmarketing heuristics and the determined likelihood of interest in theproduct.
 18. The system of claim 14, wherein the method furthercomprises: extracting the time and date information for the extractedsamples based on when the one or more social media activities wereconducted; and deriving temporal activity patterns based on statisticsfor the time and date information for the extracted samples, wherein thedetermined particular days of week and times of day is further based onthe derived temporal activity patterns.